Moving Beyond 'Driving While Black in Canada': Race, Suspect Description and Selection
Bibliographic record
Abstract
The issue of racial profiling has finally begun to attract the attention of the Canadian media; courts; human rights commission; the Canadian Bar Association; and, the academy. The focus has been on racial profiling defined as the use of racialized stereotypes of the usual suspect as the basis for suspect selection. Less attention,however, has been given to cases where race forms part of the description of a suspect provided by the victim or witness. Through the use of narrative, the article examines how race-based suspect descriptions have been misused by the police in Canada. The narratives also reveal the devastating collateral damage when the police use race in any manner in suspect selection. This damage includes widespread harassment, intimidation, false arrests, violence, death, stigmatization and an engendering of mistrust. Given the misuse, the article recommends including suspect descriptions in the racial profiling prohibition where race is used as the dominant characteristic. After considering whether there should be a complete prohibition on using race in suspect descriptions, the article examines current constitutional standards to protect against misuse and proposes a new dominant feature constitutional test.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.050 | 0.030 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".